o tBh;@srddlmZddlmZddlmZddlmZddlmZm Z Gdd d Z e d d d d d d d dddZ d S)) _get_response)auc) roc_curve)_check_pos_label_consistency)check_matplotlib_support deprecatedc@sheZdZdZddddddZdddddZedd d dddd d d Zedd ddddddZdS)RocCurveDisplayaROC Curve visualization. It is recommend to use :func:`~sklearn.metrics.RocCurveDisplay.from_estimator` or :func:`~sklearn.metrics.RocCurveDisplay.from_predictions` to create a :class:`~sklearn.metrics.RocCurveDisplay`. All parameters are stored as attributes. Read more in the :ref:`User Guide `. Parameters ---------- fpr : ndarray False positive rate. tpr : ndarray True positive rate. roc_auc : float, default=None Area under ROC curve. If None, the roc_auc score is not shown. estimator_name : str, default=None Name of estimator. If None, the estimator name is not shown. pos_label : str or int, default=None The class considered as the positive class when computing the roc auc metrics. By default, `estimators.classes_[1]` is considered as the positive class. .. versionadded:: 0.24 Attributes ---------- line_ : matplotlib Artist ROC Curve. ax_ : matplotlib Axes Axes with ROC Curve. figure_ : matplotlib Figure Figure containing the curve. See Also -------- roc_curve : Compute Receiver operating characteristic (ROC) curve. RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic (ROC) curve given an estimator and some data. RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic (ROC) curve given the true and predicted values. roc_auc_score : Compute the area under the ROC curve. Examples -------- >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from sklearn import metrics >>> y = np.array([0, 0, 1, 1]) >>> pred = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred) >>> roc_auc = metrics.auc(fpr, tpr) >>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, ... estimator_name='example estimator') >>> display.plot() <...> >>> plt.show() N)roc_aucestimator_name pos_labelcCs"||_||_||_||_||_dSN)r fprtprr r )selfrrr r r rv/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/metrics/_plot/roc_curve.py__init__Ns  zRocCurveDisplay.__init__)namec Ks$td|dur |jn|}i}|jdur%|dur%|d|jdd|d<n|jdur4d|jd|d<n|dur<||d<|jdi|ddlm}|durT|\}}|j|j|j fi|\|_ |j durnd |j dnd }d |}d |} |j || d d|vr|j dd||_|j|_|S)aOPlot visualization Extra keyword arguments will be passed to matplotlib's ``plot``. Parameters ---------- ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. name : str, default=None Name of ROC Curve for labeling. If `None`, use `estimator_name` if not `None`, otherwise no labeling is shown. Returns ------- display : :class:`~sklearn.metrics.plot.RocCurveDisplay` Object that stores computed values. RocCurveDisplay.plotNz (AUC = z0.2f)labelzAUC = z (Positive label: zFalse Positive RatezTrue Positive Rate)xlabelylabelz lower right)locr)rr r updatematplotlib.pyplotpyplotsubplotsplotrrline_r setlegendax_figurefigure_) raxrkwargs line_kwargspltfiginfo_pos_labelrrrrrr"Us0    rTauto) sample_weightdrop_intermediateresponse_methodr rr)c KsXt|jd|dur|jjn|}t||||d\} }|jd|| |||| |d| S)a Create a ROC Curve display from an estimator. Parameters ---------- estimator : estimator instance Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a classifier. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input values. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. drop_intermediate : bool, default=True Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves. response_method : {'predict_proba', 'decision_function', 'auto'} default='auto' Specifies whether to use :term:`predict_proba` or :term:`decision_function` as the target response. If set to 'auto', :term:`predict_proba` is tried first and if it does not exist :term:`decision_function` is tried next. pos_label : str or int, default=None The class considered as the positive class when computing the roc auc metrics. By default, `estimators.classes_[1]` is considered as the positive class. name : str, default=None Name of ROC Curve for labeling. If `None`, use the name of the estimator. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. **kwargs : dict Keyword arguments to be passed to matplotlib's `plot`. Returns ------- display : :class:`~sklearn.metrics.plot.RocCurveDisplay` The ROC Curve display. See Also -------- roc_curve : Compute Receiver operating characteristic (ROC) curve. RocCurveDisplay.from_predictions : ROC Curve visualization given the probabilities of scores of a classifier. roc_auc_score : Compute the area under the ROC curve. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import RocCurveDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.svm import SVC >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> clf = SVC(random_state=0).fit(X_train, y_train) >>> RocCurveDisplay.from_estimator( ... clf, X_test, y_test) <...> >>> plt.show() z.from_estimatorN)r2r )y_truey_predr0r1rr)r r)r__name__ __class__rfrom_predictions) cls estimatorXyr0r1r2r rr)r*r4rrrfrom_estimators&W zRocCurveDisplay.from_estimator)r0r1r rr)cKstt|jdt|||||d\} } } t| | } |durdn|}t||}t| | | ||d} | jd||d|S)u' Plot ROC curve given the true and predicted values. Read more in the :ref:`User Guide `. .. versionadded:: 1.0 Parameters ---------- y_true : array-like of shape (n_samples,) True labels. y_pred : array-like of shape (n_samples,) Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers). sample_weight : array-like of shape (n_samples,), default=None Sample weights. drop_intermediate : bool, default=True Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves. pos_label : str or int, default=None The label of the positive class. When `pos_label=None`, if `y_true` is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an error will be raised. name : str, default=None Name of ROC curve for labeling. If `None`, name will be set to `"Classifier"`. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. **kwargs : dict Additional keywords arguments passed to matplotlib `plot` function. Returns ------- display : :class:`~sklearn.metrics.RocCurveDisplay` Object that stores computed values. See Also -------- roc_curve : Compute Receiver operating characteristic (ROC) curve. RocCurveDisplay.from_estimator : ROC Curve visualization given an estimator and some data. roc_auc_score : Compute the area under the ROC curve. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import RocCurveDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.svm import SVC >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> clf = SVC(random_state=0).fit(X_train, y_train) >>> y_pred = clf.decision_function(X_test) >>> RocCurveDisplay.from_predictions( ... y_test, y_pred) <...> >>> plt.show() z.from_predictionsr r0r1N Classifierrrr r r r)rr)rr5rrrr r")r8r3r4r0r1r rr)r*rr_r vizrrrr7sR   z RocCurveDisplay.from_predictionsr) r5 __module__ __qualname____doc__rr" classmethodr<r7rrrrr s&C7lr zFunction :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.NTr/)r0r1r2rr)r cKsztdt||||d\} }t|| |||d\} } } t| | }|dur'|jjn|}t| | |||d}|jd||d| S)a Plot Receiver operating characteristic (ROC) curve. Extra keyword arguments will be passed to matplotlib's `plot`. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator instance Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a classifier. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input values. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. drop_intermediate : bool, default=True Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves. response_method : {'predict_proba', 'decision_function', 'auto'} default='auto' Specifies whether to use :term:`predict_proba` or :term:`decision_function` as the target response. If set to 'auto', :term:`predict_proba` is tried first and if it does not exist :term:`decision_function` is tried next. name : str, default=None Name of ROC Curve for labeling. If `None`, use the name of the estimator. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. pos_label : str or int, default=None The class considered as the positive class when computing the roc auc metrics. By default, `estimators.classes_[1]` is considered as the positive class. **kwargs : dict Additional keywords arguments passed to matplotlib `plot` function. .. versionadded:: 0.24 Returns ------- display : :class:`~sklearn.metrics.RocCurveDisplay` Object that stores computed values. See Also -------- roc_curve : Compute Receiver operating characteristic (ROC) curve. RocCurveDisplay.from_estimator : ROC Curve visualization given an estimator and some data. RocCurveDisplay.from_predictions : ROC Curve visualisation given the true and predicted values. roc_auc_score : Compute the area under the ROC curve. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn import datasets, metrics, model_selection, svm >>> X, y = datasets.make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = model_selection.train_test_split( ... X, y, random_state=0) >>> clf = svm.SVC(random_state=0) >>> clf.fit(X_train, y_train) SVC(random_state=0) >>> metrics.plot_roc_curve(clf, X_test, y_test) # doctest: +SKIP <...> >>> plt.show() plot_roc_curve)r r=Nr?r@r)rrrrr6r5r r")r9r:r;r0r1r2rr)r r*r4rrrAr rBrrrrG`s"a   rG) baserrrr_baserutilsrr r rGrrrrs$   X